How Price-Aware Are AI Agents? Rival Cuts and Legal Rules

AI agents increasingly set prices, often without anyone watching. We ask two questions. First, how price-aware are language models on their own: how do they react when a rival cuts its price and no rule constrains them? Second, how do they decide when legal rules limit when a price may change, such as platform offer holds or Germany's 2026 rule that fuel prices may rise only at noon, and can an audit still tell the model from the rule? We tested 16 open models from Chinese and US developers in the same simulated market against a scripted rival. Without rules, the models differ widely: some ignore a rival's price cut, others cut their own price by more than twice what economic theory predicts. The developer's country does not sort them, and smaller and larger versions of one family often behave differently. Under a three-period price hold, 6 of 10 models tested show a larger response to the cut, which is the pattern collusion audits search for. For one model we separate the two causes: the rule itself explains less than half of the effect, and the rest is the agent adapting its own proposals to the held price. Logging what an agent wanted to charge therefore does not clear it. For audits, we recommend recording proposed prices, published prices and rule state together, and testing the exact model under the rules it will face. Results concern audit validity in a synthetic market.